Considering how valuable data is for businesses today, how and where a business stores data has become more important than ever. Some might prefer a centralized data warehouse system, a collection of specialized data marts, or a combination to base their data analytics stack. A data mart makes it easier to access data required by a specific team or line of business within an organization.
Here’s our detailed guide about what is data mart, data lake, data warehouse, data mart, and the key benefits of data marts for businesses.
What Is a Data Mart?
A data mart is a subject-oriented database designed to make specific organizational data easy to find and readily available. A data mart is a condensed version of a data warehouse, which stores all data generated by departments of an organization. With data mart, users can quickly access relevant data and gain insights without searching through an entire data warehouse. The data held in a data mart is often controlled by a single department in an organization, like sales, finance, or marketing. Since Data marts draw data from only a few sources, they allow users to access operational data in a data warehouse within days, thus accelerating business processes. They provide cost-effective ways to gain quick, actionable insights.
Data Mart vs. Data Warehouse vs. Data Lake
Data Marts, Data Warehouses, and Data Lakes are highly structured data repositories, but they differ in the scope of data stored and serve different purposes within an organization.
The Data warehouse serves as the central repository of data for the entire organization. At the same time, data mart focuses on data important to and needed by a specific division or line of business. It aggregates data from different sources to support data mining, artificial intelligence, machine learning, which results in improved analytics and business intelligence. Since data warehouse stores all data generated by an organization, access to the warehouse should be strictly controlled. It can be extremely difficult to query data needed for a particular purpose from the enormous pool of data contained in a data warehouse. That is where the data mart is helpful. The main purpose of a data mart is to partition or separate a subset of the entire dataset to provide easy access to data to end-users.
Both data warehouse and data mart are relational databases built to store transactional data (e.g., numerical order, time value, object reference) in tabular form for ease of organizing and access.
A single data mart can be created from an existing data warehouse in the top-down development approach or from other sources like internal operational systems or external data. The designing process involves several tools and technologies to construct a physical database, populate it with data and implement stringent access and management rules. It is a complex process, but the mart enables a business to get more focused insights in less time than working with a broader dataset in a warehouse.
A Data Lake is also a data repository that provides massive storage for raw or unstructured data from various sources. Since a data lake stores raw data that is not processed or prepared for analysis, it is more accessible and cost-effective than a data warehouse. The data does not require cleanup or processing before being fed.
For more on data warehouses and data lakes, take a look at our Data Warehouse article and data lake vs. data warehouse detailed comparison.
Benefits of a Data Mart
Data Marts are built to enable business users to access the most relevant data in the shortest time. With its small size and focused design, data mart offers several benefits to the end-user, including:
- Contains data that is valuable to specific groups within an organization
- Cost-effective to build than a data warehouse.
- Allows simplified data access. Data marts contain a small subset of data, so users can easily retrieve data as needed compared to sifting through broader data set from a data warehouse.
- Quick access to data insights. Insights gained from a data mart impacts decisions at the department level. Teams can use these focused insights with specific goals in mind, resulting in faster business processes and higher productivity.
- Data mart needs less Implementation Time compared to data warehouse because you only need to focus on a small subset of data. Implementation tends to be more efficient and less time-consuming.
- It contains historical data, which helps data analysts to predict data trends.
Types of Data Marts
There are three main types of data mart:
1. Dependent Data Mart - Built by drawing data directly from an existing data warehouse. All business data is stored in a centralized repository, and then a well-defined set of data is extracted when needed for analysis. The specific data set is aggregated from the warehouse, restructured, and populated into the data mart for querying. It is usually a logical view or physical subset of the data warehouse.
2. Independent Data Mart – stand-alone system, built without the use of a central data warehouse. Independent Data Marts are ideal for smaller units within an organization. Data is obtained from internal or external data sources, processed, loaded, and stored in the data mart until queried later for business analytics.
3. Hybrid Data Mart – it combines data from the data warehouse and other operational sources. A hybrid data mart is best suited for multiple database environments with a fast implementation turnaround. The system requires the least data cleansing effort.
Structure of a Data Mart
A data mart and a data warehouse can be organized using a star, vault, snowflake, or other schema as a blueprint.
Usually, a star schema is used that consists of one or many fact tables, referencing dimensional tables in a relational database. In a star schema, fewer joints are required for writing queries.
In the snowflake schema, there’s no clear definition of dimensions. They are normalized, so data redundancy gets reduced, and data integrity is protected. The structure is complicated and difficult to maintain, though it takes less space to store dimension tables.
Data Mart and Cloud Architecture
Businesses are increasingly moving to cloud-based data marts and data warehouses instead of traditional on-premises setups. Business and IT teams are striving to become more agile and data-driven to improve regular decision-making. The benefits of cloud architecture include:
- Decreases need to purchase physical hardware
- Decreases need for manual intervention
- Faster and cheaper to set up and implement cloud data marts
- The cloud-based architecture uses massively parallel processing; hence, data marts can perform complex analytical queries much faster.
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The Future of Data Marts Is in the Cloud
Leading cloud service providers provide a shared cloud-based platform to create and store data, access, and analyze efficiently. Business teams can quickly combine transient data clusters for short-term analysis or long-lived clusters for sustained work. With the use of modern technologies, data storage can be easily separated from computing, allowing for extensive scalability for querying data.
Key advantages of cloud-based data marts are:
- Flexible architecture
- Single depository housing all data marts
- On-demand consumption of Resources
- Real-time access to Information
- Higher Efficiency
- Interactive Analytics in Realtime
- Consolidation of Resources that cost less
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